proppy/zerotoasic_project1_1.ipynb

## zerotoasic_project1_1.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "zerotoasic_project1_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPUmkI3y/fqxDdspUN6Yr8o",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/proppy/964fa4b9277c3baf9e731872bbad93e4/zerotoasic_project1_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# sky130_fd_sc_hd__nand2_1 simulation"
      ],
      "metadata": {
        "id": "4JefcrFMsZKI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "o6JZnKErwNSk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7WQfknLlY0a",
        "outputId": "10f1e894-817b-4d8b-c8bc-95f26a2dcdd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:34\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ],
      "source": [
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import condacolab\n",
        "condacolab.check()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uq4xbYT8RFUi",
        "outputId": "e9912e7f-3a08-4c9f-9f71-5ba685173500"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda install -c conda-forge ngspice-lib\n",
        "!pip install pyspice\n",
        "!pip install cffi --upgrade\n",
        "# restart runtime to use upgraded cffi\n",
        "import IPython\n",
        "IPython.Application.instance().kernel.do_shutdown(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fm4j_ZRKRJ7K",
        "outputId": "e4726c8c-805a-43b5-aaf4-41a3782557fe"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "\n",
            "# All requested packages already installed.\n",
            "\n",
            "Requirement already satisfied: pyspice in /usr/local/lib/python3.7/site-packages (1.5)\n",
            "Requirement already satisfied: numpy>=1.18 in /usr/local/lib/python3.7/site-packages (from pyspice) (1.21.5)\n",
            "Requirement already satisfied: requests>=2.23 in /usr/local/lib/python3.7/site-packages (from pyspice) (2.25.1)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.7/site-packages (from pyspice) (6.0)\n",
            "Requirement already satisfied: ply>=3.11 in /usr/local/lib/python3.7/site-packages (from pyspice) (3.11)\n",
            "Requirement already satisfied: matplotlib>=3.2 in /usr/local/lib/python3.7/site-packages (from pyspice) (3.5.1)\n",
            "Requirement already satisfied: scipy>=1.4 in /usr/local/lib/python3.7/site-packages (from pyspice) (1.7.3)\n",
            "Requirement already satisfied: cffi>=1.14 in /usr/local/lib/python3.7/site-packages (from pyspice) (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/site-packages (from cffi>=1.14->pyspice) (2.20)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.2->pyspice) (4.28.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.2->pyspice) (9.0.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.2->pyspice) (21.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.2->pyspice) (2.8.2)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.2->pyspice) (3.0.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.2->pyspice) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.2->pyspice) (1.3.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib>=3.2->pyspice) (1.15.0)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/site-packages (from requests>=2.23->pyspice) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests>=2.23->pyspice) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/site-packages (from requests>=2.23->pyspice) (2021.10.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/site-packages (from requests>=2.23->pyspice) (1.26.3)\n",
            "Requirement already satisfied: cffi in /usr/local/lib/python3.7/site-packages (1.15.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/site-packages (from cffi) (2.20)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'restart': True, 'status': 'ok'}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/google/skywater-pdk-libs-sky130_fd_pr.git\n",
        "!git clone https://github.com/google/skywater-pdk-libs-sky130_fd_sc_hd.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Up9R17Y8yh7r",
        "outputId": "42ee5f9f-e293-4a3c-d924-0062881567ff"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'skywater-pdk-libs-sky130_fd_pr'...\n",
            "remote: Enumerating objects: 5594, done.\u001b[K\n",
            "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 5594 (delta 0), reused 9 (delta 0), pack-reused 5585\u001b[K\n",
            "Receiving objects: 100% (5594/5594), 111.21 MiB | 25.20 MiB/s, done.\n",
            "Resolving deltas: 100% (4839/4839), done.\n",
            "Checking out files: 100% (3820/3820), done.\n",
            "Cloning into 'skywater-pdk-libs-sky130_fd_sc_hd'...\n",
            "remote: Enumerating objects: 41249, done.\u001b[K\n",
            "remote: Total 41249 (delta 0), reused 0 (delta 0), pack-reused 41249\u001b[K\n",
            "Receiving objects: 100% (41249/41249), 211.86 MiB | 27.05 MiB/s, done.\n",
            "Resolving deltas: 100% (38730/38730), done.\n",
            "Checking out files: 100% (13114/13114), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Simulation"
      ],
      "metadata": {
        "id": "TGgki8I-wPWa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PySpice.Spice.Netlist import Circuit\n",
        "from PySpice.Unit import *\n",
        "\n",
        "circuit = Circuit('sky130_fd_sc_hd__nand2_1')\n",
        "circuit.lib('./skywater-pdk-libs-sky130_fd_pr/models/sky130.lib.spice', 'tt')\n",
        "circuit.include('./skywater-pdk-libs-sky130_fd_sc_hd/cells/nand2/sky130_fd_sc_hd__nand2_1.spice')\n",
        "circuit.X('cell', 'sky130_fd_sc_hd__nand2_1', 'A', 'B', 'VGND', 'VNB', 'VPB', 'VPWR', 'Y')\n",
        "circuit.V('gnd', 'VGND', 0, 0)\n",
        "circuit.V('dd', 'VPWR', 'VGND', 1.8)\n",
        "circuit.PulseVoltageSource('a', 'A', 'VGND',\n",
        "                                    initial_value=0@u_V, pulsed_value=1.8@u_V,\n",
        "                                    rise_time=10@u_ps, fall_time=10@u_ps,\n",
        "                                    pulse_width=1@u_ns, period=2@u_ns, delay_time=1@u_ns)\n",
        "circuit.PulseVoltageSource('b', 'B', 'VGND',\n",
        "                                    initial_value=0@u_V, pulsed_value=1.8@u_V,\n",
        "                                    rise_time=10@u_ps, fall_time=10@u_ps,\n",
        "                                    pulse_width=1@u_ns, period=2@u_ns, delay_time=1.5@u_ns)\n",
        "print(str(circuit))\n",
        "simulator = circuit.simulator()\n",
        "analysis = simulator.transient(step_time=10@u_ps, end_time=3@u_ns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81_jvmsqqgCW",
        "outputId": "2340a755-de6d-4186-d7f0-83b561a2f8d9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".title sky130_fd_sc_hd__nand2_1\n",
            ".include /content/skywater-pdk-libs-sky130_fd_sc_hd/cells/nand2/sky130_fd_sc_hd__nand2_1.spice\n",
            ".lib /content/skywater-pdk-libs-sky130_fd_pr/models/sky130.lib.spice tt\n",
            "Xcell A B VGND VNB VPB VPWR Y sky130_fd_sc_hd__nand2_1\n",
            "Vgnd VGND 0 0\n",
            "Vdd VPWR VGND 1.8\n",
            "Va A VGND DC 0V PULSE(0V 1.8V 1ns 10ps 10ps 1ns 2ns)\n",
            "Vb B VGND DC 0V PULSE(0V 1.8V 1.5ns 10ps 10ps 1ns 2ns)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: include: has no value, DC 0 assumed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "ax.set_title('sky130_fd_sc_hd__nand2_1')\n",
        "ax.set_xlabel('time in 1e-14s')\n",
        "ax.set_ylabel('voltage in V')\n",
        "ax.plot(analysis.A)\n",
        "ax.plot(analysis.B)\n",
        "ax.plot(analysis.Y)\n",
        "ax.legend(('A', 'B', 'Y'))\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "UgyBda7z6udF",
        "outputId": "981133c8-6e14-4895-8f71-4b7455ffb0d1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}